Wearable AI Reveals the Impact of Intermittent Fasting on Stress Levels in School Children During Ramadan.

Intermittent fasting has been practiced for centuries across many cultures globally. Recently many studies have reported intermittent fasting for its lifestyle benefits, the major shift in eating habits and patterns is associated with several changes in hormones and circadian rhythms. Whether there are accompanying changes in stress levels is not widely reported especially in school children.

The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review.

Background: In 2021 alone, diabetes mellitus, a metabolic disorder primarily characterized by abnormally high blood glucose (BG) levels, affected 537 million people globally, and over 6 million deaths were reported. The use of noninvasive technologies, such as wearable devices (WDs), to regulate and monitor BG in people with diabetes is a relatively new concept and yet in its infancy. Noninvasive WDs coupled with machine learning (ML) techniques have the potential to understand and conclude meaningful information from the gathered data and provide clinically meaningful advanced analytics for the purpose of forecasting or prediction.

Correction: Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review.

[This corrects the article DOI: 10.2196/42672.].

Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review.

Background: Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of artificial intelligence (AI) into wearable devices (wearable AI) has been exploited to provide mental health services.

Objective: This review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues.

Analysis of risk factors progression of preterm delivery using electronic health records.

Background: Preterm deliveries have many negative health implications on both mother and child. Identifying the population level factors that increase the risk of preterm deliveries is an important step in the direction of mitigating the impact and reducing the frequency of occurrence of preterm deliveries. The purpose of this work is to identify preterm delivery risk factors and their progression throughout the pregnancy from a large collection of Electronic Health Records (EHR).

Overview of Artificial Intelligence-Driven Wearable Devices for Diabetes: Scoping Review.

Background: Prevalence of diabetes has steadily increased over the last few decades with 1.5 million deaths reported in 2012 alone. Traditionally, analyzing patients with diabetes has remained a largely invasive approach.

Characteristic MicroRNAs Linked to Dysregulated Metabolic Pathways in Qatari Adult Subjects With Obesity and Metabolic Syndrome.

Background: Obesity-associated dysglycemia is associated with metabolic disorders. MicroRNAs (miRNAs) are known regulators of metabolic homeostasis. We aimed to assess the relationship of circulating miRNAs with clinical features in obese Qatari individuals.

Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast during ramadan (The PROFAST - IT Ramadan study).

Objective: To develop a machine-based algorithm from clinical and demographic data, physical activity and glucose variability to predict hyperglycaemic and hypoglycaemic excursions in patients with type 2 diabetes on multiple glucose lowering therapies who fast during Ramadan.

Patients And Methods: Thirteen patients (10 males and three females) with type 2 diabetes on 3 or more anti-diabetic medications were studied with a Fitbit-2 pedometer device and Freestyle Libre (Abbott Diagnostics) 2 weeks before and 2 weeks during Ramadan. Several machine learning techniques were trained to predict blood glucose levels in a regression framework utilising physical activity and contemporaneous blood glucose levels, comparing Ramadan to non-Ramadan days.

Validation of the Internet Addiction Test in Students at a Pakistani Medical and Dental School.

Despite growing concerns over pathological internet usage, studies based on validated psychometric instruments are still lacking in Pakistan. This study aimed to examine the psychometric properties of the Internet Addiction Test (IAT) in a sample of Pakistani students. A total of 522 students of medicine and dentistry completed the questionnaire, which consisted of four sections: (a) demographics, (b) number of hours spent on the Internet per day, (c) English version of the IAT, and (d) the Defense Style Questionnaire-40.

Predicting readmission risk with institution-specific prediction models.

Objective: The ability to predict patient readmission risk is extremely valuable for hospitals, especially under the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services which went into effect starting October 1, 2012. There is a plethora of work in the literature that deals with developing readmission risk prediction models, but most of them do not have sufficient prediction accuracy to be deployed in a clinical setting, partly because different hospitals may have different characteristics in their patient populations.

Methods And Materials: We propose a generic framework for institution-specific readmission risk prediction, which takes patient data from a single institution and produces a statistical risk prediction model optimized for that particular institution and, optionally, for a specific condition.

Categorizing medications from unstructured clinical notes.

One of the important pieces of information in a patient's clinical record is the information about their medications. Besides administering information, it also consists of the category of the medication i.e.

Robust extraction of P300 using constrained ICA for BCI applications.

P300 is a positive event-related potential used by P300-brain computer interfaces (BCIs) as a means of communication with external devices. One of the main requirements of any P300-based BCI is accuracy and time efficiency for P300 extraction and detection. Among many attempted techniques, independent component analysis (ICA) is currently the most popular P300 extraction technique.

Leveraging rich annotations to improve learning of medical concepts from clinical free text.

Information extraction from clinical free text is one of the key elements in medical informatics research. In this paper we propose a general framework to improve learning-based information extraction systems with the help of rich annotations (i.e.

Automated identification of medical concepts and assertions in medical text.

This paper describes a machine learning, text processing approach that allows the extraction of key medical information from unstructured text in Electronic Medical Records. The approach utilizes a novel text representation that shares the simplicity of the widely used bag-of-words representation, but can also represent some form of semantic information in the text. The large dimensionality of this type of learning models is controlled by the use of a ℓ(1) regularization to favor parsimonious models.

Protocols for establishment of an in vitro collection of medicinal plants in the genus Scutellaria.

The study of medicinal plants has many unique challenges and special considerations. These plants are studied for their specific chemistry, or pharmacologic activity. Plants are highly sensitive to their environment and respond through changes in their chemistry.